Metal oxide (MeO) nanoparticles (NPs) have become common in our everyday life over the past years. However, there is still an important knowledge gap regarding their toxicological effect and, in particular, how the different physical and chemical properties of MeO NPs influence their cytotoxicity and the subsequent implications for risk assessment. This work analyses the physicochemical properties of MeO NPs that have been reported as relevant for risk assessment and the experimental and theoretical methods used to obtain them. The surface, physical and chemical properties of NPs have been critically revisited to shed light on the features that can cause toxicity. Due to the large number of existing MeO NPs, in silico studies are necessary to get a good understanding of the NPs' physicochemical properties; therefore this review focuses on the state of the art computational methods used to model MeO NP toxicity: QSAR and QSTR models and their alternative approaches provide a better understanding of MeO NP biological toxicity in organisms.

Titanium dioxide is a key material in many fields, including technological, industrial and biomedical applications. Many of these applications are related to the surface reactivity of TiO₂ and involve its reducibility properties. Recently titania has been banned as a food additive due to its (nano)toxicity, and the release of reactive oxygen species plays a crucial role in many toxicological mechanisms. Determining chemical descriptors that account for the extension of reduction is necessary to understand such processes and necessary for predicting the reactivity of an unknown system. In the present work, we compute a set of chemical descriptors for selected surfaces of anatase and rutile TiO₂. The aim is twofold: we want to provide chemically meaningful information on the surface reactivity, and benchmark the descriptors for two ab initio schemes. To do so, we compute the oxygen vacancy formation energy, and the corresponding electronic structure, in four slab models with two different computational schemes (DFT+U and DFTB). In this way, we characterize the robustness of the dataset, with the purpose of scaling up to more realistic model systems such as nanoparticles or explicit solvent, which are too computationally demanding for state-of-the-art density functional theory approaches.

Metal oxides (MOs) are key materials in many fields, including technological, industrial, and biomedical applications. In most of these implementations, surface reactivity and reducibility properties are critical considerations. In their nanosized form, MOs exhibit enhanced reactivity that is connected to toxicity. Besides the fact that the biological molecule and the surface of the corresponding material interact chemically, little is known about the toxicological mechanisms involved on the atomic scale. The goal of this study is to investigate the role of TiO₂ surfaces in interaction with one genetic base, namely guanine. Using a combination of the quasi-electronic density functional-tight binding molecular dynamics simulations and density functional theory calculations, we explored the adsorption modes of guanine with a stoichiometric and oxygen-deficient anatase TiO₂ (101) surface. With such an approach, we have characterized new adsorption modes not previously found, and we have highlighted the relevance of defective surfaces in the adsorption of genetic basis, as a model for explaining possible toxicology mechanisms induced by the adsorption process.

Metal oxides (MOs) are the key materials in applications of biomedicine industrial technologies due to their versatile features. Knowing their possible toxicity level is crucial given some specific environments, particularly in water. We have learned that their reactivity almost depends on the electronic structure on the surface of the MOs. Thus, a detailed understanding of the electronic structure on the surface and its reactivity processes is useful for determining the toxicity in MOs and defining good descriptive parameters. We simulated the interaction of ZnO and TiO₂ slab models with water and checked their geometric and electronic structure changes from the bulk of the material to the water interface. To this end, we used the density functional tight binding theory coupled with finite temperature molecular dynamics. We have observed the interaction of water with the MO surface in terms of electronic and geometric parameters for several conditions, such as temperature, hydrogenated or clean, and exposed surface. In doing so, we provide molecular-level insights into topographical and electronic processes on MO surfaces besides finding critical points on the surface that can explain the initialization of dissolution processes. Thus, we reveal information about potential toxicity descriptors in a systematic analysis approach.

The use of density functional theory (DFT) simulations has proved to be a powerful tool in the Earth and Environmental Sciences. However, these calculations require a large computational power and are limited to ~1000 atoms in big supercomputers. An alternative to parallelize DFT codes in CPU supercomputers is the use of GPU cards. These contain a large number of threads that can accelerate codes which are properly programmed for parallel calculations. In this mini-review we have evaluated which factors are crucial to obtain an appropriate acceleration in the process of moving CPU codes to their GPU version: memory transfer, work flows and CPU/GPU ratio. Accelerations up to 20-40 times the pure CPU version of the DFT code have been achieved. This makes that the additional cost of GPUs cards is less than the price/performance obtained.